Why Mastermind?

Since nobody is reviewing this, I’m not giving a long answer this question posed by one of the reviewers of a paper I submitted ages ago, in the nineties, proposing solutions using evolutionary algorithms to the game. However, the short question, as happens with most everything in Science, it’s because it’s there. However, after this empirical study of exhaustive solutions it is a bit closer.
In this paper, which is a draft of something we intend to submit to a journal in the near future, we describe something that has been disregarded in Mastermind solutions: the pure chance of drawing the correct solution in the opening moves. In fact, this chance dominates in the first moves, until the search space is reduced to a single solution, which is usually the intention of most empirical solutions to the game.
Which means that a method that is able to increase that chance, will be able to beat traditional solutions. In fact, it does not, but it is consistently as good as the best solution for each size.
It is rather a longish paper (and might become even more so before submission), but you might learn a thing or two about Mastermind. Besides, it is intended as a base for future papers that will apply our usual techniques, evolutionary algorithms.

Going a bit farther (and a bit faster) solving MasterMind using evolutionary algorithms

We left MasterMind last year in a good state using estimation of distribution algorithms; however, if we want to find a solution for higher dimensions (more colors, more pegs) we have to improve the number of evaluations. In this case we use something we call endgames; same as chess playing algorithms use a database of endgames for ending a game in a straightforward way, in MasterMind we can recognize a few occasions in which the search space is reduced drastically and it’s better either change the strategy or just change the search space. When we know the colors (that is, we obtain as many white+blacks as the length of the combination) the best is to just revert to exhaustive search over combination space; when the answer is 0 whites/blacks we can also exclude those colors from the search space and start, maybe with a smaller population.
This is what we do in the paper Improving and Scaling Evolutionary Approaches to the MasterMind Problem , which was presented a short time ago in the EvoGames workshop in Torino

During the presentation, Carlos Cotta and Carlos Fernandes played the game shown above.
Here’s the presentation, which you can download at ease. Picture credits are included in the notes.